The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors

Abstract

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective-stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5Њ averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5Њ grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5Њ grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI-and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low...

Afternoon rain more likely over drier soils

“…For each month of the year and each overpass (am or pm), we correlated the change in soil moisture between consecutive observations with precipitation in the intervening period. In the case of the AMSR-E data, we first excluded cases where rainfall was present at overpass time from the Goddard Profiling Algorithm [GPROF; Kummerow et al, 2001]. Only pixels with a positive linear correlation (p<0.25) were retained.…”

TRMM 2A12 Land Precipitation Product - Status and Future Plans

“…The TMI precipitation algorithm (also known as the Goddard Profiling Algorithm or GPROF) uses a Bayesian approach to match observed brightness temperatures with those from a database of simulated brightness temperatures through a radiative transfer model coupled with hydrometeor profiles from cloudresolving model simulations (Kummerow et al 2001). Over-ocean precipitation retrievals make use of the strong contrast between the warm, un-polarized emission from rainfall and the cold, polarized ocean surface.…”

Verification of Hydrometeor Properties Simulated by a Cloud-Resolving Model Using a Passive Microwave Satellite and Ground-Based Radar Observations for a Rainfall System Associated with the Baiu Front

“…Cloud resolving models (CRMs) with complicated cloud microphysical parameterizations explicitly predict various hydrometeors at high time and space resolution; therefore, CRMs serve as valuable tools for satellite remote sensing of precipitation for inferring information about precipitating clouds that cannot be directly observed (Adler et al 1991;Smith et al 1994;Kummerow et al 1996Kummerow et al , 2001Panegrossi et al 1998;Olson et al 2006). However, to use CRMs in precipitation remote sensing, their output must be verified with observational data to confirm that the information derived from them is reliable.…”